Developing an AI application

Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications.

In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using this dataset of 102 flower categories, you can see a few examples below.

The project is broken down into multiple steps:

  • Load and preprocess the image dataset
  • Train the image classifier on your dataset
  • Use the trained classifier to predict image content

We'll lead you through each part which you'll implement in Python.

When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.

First up is importing the packages you'll need. It's good practice to keep all the imports at the beginning of your code. As you work through this notebook and find you need to import a package, make sure to add the import up here.

Imports

In [1]:
# Imports here
import seaborn as sns
%matplotlib inline
%config InlineBackend.figure_format = 'retina'

import matplotlib.pyplot as plt

import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
from torchvision import datasets, transforms, models

import numpy as np
from PIL import Image

import pandas as pd

import os 
os.getcwd()

import glob

import json

# Udacity file had a hyphen in its name. I renamed the file from workspace-utils.py to workspace_utils.py
from workspace_utils import active_session
In [2]:
# import helper

# ---------------------------------------------------------------------------
# ModuleNotFoundError                       Traceback (most recent call last)
# <ipython-input-89-b1da2f10f7bc> in <module>()
# ----> 1 import helper

# ModuleNotFoundError: No module named 'helper'

Load the data

Here you'll use torchvision to load the data (documentation). The data should be included alongside this notebook, otherwise you can download it here. The dataset is split into three parts, training, validation, and testing. For the training, you'll want to apply transformations such as random scaling, cropping, and flipping. This will help the network generalize leading to better performance. You'll also need to make sure the input data is resized to 224x224 pixels as required by the pre-trained networks.

The validation and testing sets are used to measure the model's performance on data it hasn't seen yet. For this you don't want any scaling or rotation transformations, but you'll need to resize then crop the images to the appropriate size.

The pre-trained networks you'll use were trained on the ImageNet dataset where each color channel was normalized separately. For all three sets you'll need to normalize the means and standard deviations of the images to what the network expects. For the means, it's [0.485, 0.456, 0.406] and for the standard deviations [0.229, 0.224, 0.225], calculated from the ImageNet images. These values will shift each color channel to be centered at 0 and range from -1 to 1.

In [2]:
# directories
data_dir = 'flowers'
train_dir = data_dir + '/train'
valid_dir = data_dir + '/valid'
test_dir = data_dir + '/test'
In [3]:
# TODO: Define your transforms for the training, validation, and testing sets
"""
Source: Udacity Deep Learning with Pytorch lesson

"""
train_transforms = transforms.Compose([transforms.RandomRotation(30),
                                       transforms.RandomResizedCrop(224),
                                       transforms.RandomHorizontalFlip(),
                                       transforms.ToTensor(),
                                       transforms.Normalize([0.485, 0.456, 0.406],
                                                            [0.229, 0.224, 0.225])])

valid_transforms = transforms.Compose([transforms.Resize(256),
                                      transforms.CenterCrop(224),
                                      transforms.ToTensor(),
                                      transforms.Normalize([0.485, 0.456, 0.406],
                                                           [0.229, 0.224, 0.225])])

test_transforms = transforms.Compose([transforms.Resize(256),
                                      transforms.CenterCrop(224),
                                      transforms.ToTensor(),
                                      transforms.Normalize([0.485, 0.456, 0.406],
                                                           [0.229, 0.224, 0.225])])

# TODO: Load the datasets with ImageFolder
train_data = datasets.ImageFolder(train_dir, transform=train_transforms)
valid_data = datasets.ImageFolder(valid_dir, transform=valid_transforms)
test_data = datasets.ImageFolder(test_dir, transform=test_transforms)

# TODO: Using the image datasets and the trainforms, define the dataloaders
trainloader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)
validloader = torch.utils.data.DataLoader(valid_data, batch_size=64, shuffle=True)
testloader = torch.utils.data.DataLoader(test_data, batch_size=64, shuffle=True)
In [4]:
# https://discuss.pytorch.org/t/about-the-relation-between-batch-size-and-length-of-data-loader/10510/2
# my understanding is that len(dataloader) = len(dataset) / batch_size
print('length of data loaders')
print(len(trainloader))
print(len(validloader))
print(len(testloader))
length of data loaders
103
13
13
In [40]:
# this looks like the size of train set
103*64
Out[40]:
6592
In [45]:
# This looks like the size of test set. Valid data has the same size
13*64
Out[45]:
832
In [44]:
# (valid_data + test_data) / all_data
832*2/(6592+832*2)
Out[44]:
0.20155038759689922
In [47]:
# (test_data) / all_data
832/(6592+832*2)
Out[47]:
0.10077519379844961
In [5]:
def show_Image(loader):
    """
    credit: adapted from https://github.com/Surya-Prakash-Reddy/Classifying-Cats-and-Dogs
    by Surya Prakash Reddy
    """

    data_iter = iter(loader)
    images, labels = next(data_iter)
    img = images[0].cpu().numpy().transpose(1,2,0)

    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    img = std * img + mean
    img = np.clip(img, 0, 1)
    plt.imshow(img)
In [12]:
show_Image(trainloader)
In [13]:
show_Image(validloader)
In [14]:
show_Image(testloader)

Label mapping

You'll also need to load in a mapping from category label to category name. You can find this in the file cat_to_name.json. It's a JSON object which you can read in with the json module. This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers.

In [6]:
with open('cat_to_name.json', 'r') as f:
    cat_to_name = json.load(f)
In [9]:
cat_to_name['1']
Out[9]:
'pink primrose'
In [10]:
cat_to_name
Out[10]:
{'21': 'fire lily',
 '3': 'canterbury bells',
 '45': 'bolero deep blue',
 '1': 'pink primrose',
 '34': 'mexican aster',
 '27': 'prince of wales feathers',
 '7': 'moon orchid',
 '16': 'globe-flower',
 '25': 'grape hyacinth',
 '26': 'corn poppy',
 '79': 'toad lily',
 '39': 'siam tulip',
 '24': 'red ginger',
 '67': 'spring crocus',
 '35': 'alpine sea holly',
 '32': 'garden phlox',
 '10': 'globe thistle',
 '6': 'tiger lily',
 '93': 'ball moss',
 '33': 'love in the mist',
 '9': 'monkshood',
 '102': 'blackberry lily',
 '14': 'spear thistle',
 '19': 'balloon flower',
 '100': 'blanket flower',
 '13': 'king protea',
 '49': 'oxeye daisy',
 '15': 'yellow iris',
 '61': 'cautleya spicata',
 '31': 'carnation',
 '64': 'silverbush',
 '68': 'bearded iris',
 '63': 'black-eyed susan',
 '69': 'windflower',
 '62': 'japanese anemone',
 '20': 'giant white arum lily',
 '38': 'great masterwort',
 '4': 'sweet pea',
 '86': 'tree mallow',
 '101': 'trumpet creeper',
 '42': 'daffodil',
 '22': 'pincushion flower',
 '2': 'hard-leaved pocket orchid',
 '54': 'sunflower',
 '66': 'osteospermum',
 '70': 'tree poppy',
 '85': 'desert-rose',
 '99': 'bromelia',
 '87': 'magnolia',
 '5': 'english marigold',
 '92': 'bee balm',
 '28': 'stemless gentian',
 '97': 'mallow',
 '57': 'gaura',
 '40': 'lenten rose',
 '47': 'marigold',
 '59': 'orange dahlia',
 '48': 'buttercup',
 '55': 'pelargonium',
 '36': 'ruby-lipped cattleya',
 '91': 'hippeastrum',
 '29': 'artichoke',
 '71': 'gazania',
 '90': 'canna lily',
 '18': 'peruvian lily',
 '98': 'mexican petunia',
 '8': 'bird of paradise',
 '30': 'sweet william',
 '17': 'purple coneflower',
 '52': 'wild pansy',
 '84': 'columbine',
 '12': "colt's foot",
 '11': 'snapdragon',
 '96': 'camellia',
 '23': 'fritillary',
 '50': 'common dandelion',
 '44': 'poinsettia',
 '53': 'primula',
 '72': 'azalea',
 '65': 'californian poppy',
 '80': 'anthurium',
 '76': 'morning glory',
 '37': 'cape flower',
 '56': 'bishop of llandaff',
 '60': 'pink-yellow dahlia',
 '82': 'clematis',
 '58': 'geranium',
 '75': 'thorn apple',
 '41': 'barbeton daisy',
 '95': 'bougainvillea',
 '43': 'sword lily',
 '83': 'hibiscus',
 '78': 'lotus lotus',
 '88': 'cyclamen',
 '94': 'foxglove',
 '81': 'frangipani',
 '74': 'rose',
 '89': 'watercress',
 '73': 'water lily',
 '46': 'wallflower',
 '77': 'passion flower',
 '51': 'petunia'}

Building and training the classifier

Now that the data is ready, it's time to build and train the classifier. As usual, you should use one of the pretrained models from torchvision.models to get the image features. Build and train a new feed-forward classifier using those features.

We're going to leave this part up to you. Refer to the rubric for guidance on successfully completing this section. Things you'll need to do:

  • Load a pre-trained network (If you need a starting point, the VGG networks work great and are straightforward to use)
  • Define a new, untrained feed-forward network as a classifier, using ReLU activations and dropout
  • Train the classifier layers using backpropagation using the pre-trained network to get the features
  • Track the loss and accuracy on the validation set to determine the best hyperparameters

We've left a cell open for you below, but use as many as you need. Our advice is to break the problem up into smaller parts you can run separately. Check that each part is doing what you expect, then move on to the next. You'll likely find that as you work through each part, you'll need to go back and modify your previous code. This is totally normal!

When training make sure you're updating only the weights of the feed-forward network. You should be able to get the validation accuracy above 70% if you build everything right. Make sure to try different hyperparameters (learning rate, units in the classifier, epochs, etc) to find the best model. Save those hyperparameters to use as default values in the next part of the project.

One last important tip if you're using the workspace to run your code: To avoid having your workspace disconnect during the long-running tasks in this notebook, please read in the earlier page in this lesson called Intro to GPU Workspaces about Keeping Your Session Active. You'll want to include code from the workspace_utils.py module.

Note for Workspace users: If your network is over 1 GB when saved as a checkpoint, there might be issues with saving backups in your workspace. Typically this happens with wide dense layers after the convolutional layers. If your saved checkpoint is larger than 1 GB (you can open a terminal and check with ls -lh), you should reduce the size of your hidden layers and train again.

In [26]:
# TODO: Build and train your network
In [15]:
model = models.vgg16(pretrained=True)
print(model)
VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace)
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace)
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (6): ReLU(inplace)
    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): ReLU(inplace)
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace)
    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): ReLU(inplace)
    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): ReLU(inplace)
    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): ReLU(inplace)
    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (20): ReLU(inplace)
    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (22): ReLU(inplace)
    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (25): ReLU(inplace)
    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (27): ReLU(inplace)
    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (29): ReLU(inplace)
    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (classifier): Sequential(
    (0): Linear(in_features=25088, out_features=4096, bias=True)
    (1): ReLU(inplace)
    (2): Dropout(p=0.5)
    (3): Linear(in_features=4096, out_features=4096, bias=True)
    (4): ReLU(inplace)
    (5): Dropout(p=0.5)
    (6): Linear(in_features=4096, out_features=1000, bias=True)
  )
)
In [10]:
model = models.vgg19_bn(pretrained=True)
model
Out[10]:
VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace)
    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU(inplace)
    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (9): ReLU(inplace)
    (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (12): ReLU(inplace)
    (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (16): ReLU(inplace)
    (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (19): ReLU(inplace)
    (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (22): ReLU(inplace)
    (23): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (24): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (25): ReLU(inplace)
    (26): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (27): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (29): ReLU(inplace)
    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (32): ReLU(inplace)
    (33): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (34): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (35): ReLU(inplace)
    (36): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (37): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (38): ReLU(inplace)
    (39): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (42): ReLU(inplace)
    (43): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (44): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (45): ReLU(inplace)
    (46): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (47): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (48): ReLU(inplace)
    (49): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (50): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (51): ReLU(inplace)
    (52): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (classifier): Sequential(
    (0): Linear(in_features=25088, out_features=4096, bias=True)
    (1): ReLU(inplace)
    (2): Dropout(p=0.5)
    (3): Linear(in_features=4096, out_features=4096, bias=True)
    (4): ReLU(inplace)
    (5): Dropout(p=0.5)
    (6): Linear(in_features=4096, out_features=1000, bias=True)
  )
)
In [11]:
model.classifier
Out[11]:
Sequential(
  (0): Linear(in_features=25088, out_features=4096, bias=True)
  (1): ReLU(inplace)
  (2): Dropout(p=0.5)
  (3): Linear(in_features=4096, out_features=4096, bias=True)
  (4): ReLU(inplace)
  (5): Dropout(p=0.5)
  (6): Linear(in_features=4096, out_features=1000, bias=True)
)
In [12]:
# Use GPU if it's available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
In [13]:
# Freeze parameters so we don't backprop through them
for param in model.parameters():
    param.requires_grad = False
    
model.classifier = nn.Sequential(
                    nn.Linear(25088, 4096),
                    nn.ReLU(),
                    nn.Dropout(p=0.2),
                    nn.Linear(4096, 1024),
                    nn.ReLU(),
                    nn.Dropout(p=0.2),
                    nn.Linear(1024, 102),
                    nn.LogSoftmax(dim=1)
)

criterion = nn.NLLLoss()

# Only train the classifier parameters, feature parameters are frozen
optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)

model.to(device)
Out[13]:
VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace)
    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU(inplace)
    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (9): ReLU(inplace)
    (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (12): ReLU(inplace)
    (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (16): ReLU(inplace)
    (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (19): ReLU(inplace)
    (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (22): ReLU(inplace)
    (23): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (24): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (25): ReLU(inplace)
    (26): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (27): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (29): ReLU(inplace)
    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (32): ReLU(inplace)
    (33): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (34): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (35): ReLU(inplace)
    (36): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (37): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (38): ReLU(inplace)
    (39): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (42): ReLU(inplace)
    (43): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (44): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (45): ReLU(inplace)
    (46): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (47): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (48): ReLU(inplace)
    (49): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (50): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (51): ReLU(inplace)
    (52): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (classifier): Sequential(
    (0): Linear(in_features=25088, out_features=4096, bias=True)
    (1): ReLU()
    (2): Dropout(p=0.2)
    (3): Linear(in_features=4096, out_features=1024, bias=True)
    (4): ReLU()
    (5): Dropout(p=0.2)
    (6): Linear(in_features=1024, out_features=102, bias=True)
    (7): LogSoftmax()
  )
)
In [14]:
model
Out[14]:
VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace)
    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU(inplace)
    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (9): ReLU(inplace)
    (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (12): ReLU(inplace)
    (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (16): ReLU(inplace)
    (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (19): ReLU(inplace)
    (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (22): ReLU(inplace)
    (23): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (24): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (25): ReLU(inplace)
    (26): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (27): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (29): ReLU(inplace)
    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (32): ReLU(inplace)
    (33): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (34): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (35): ReLU(inplace)
    (36): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (37): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (38): ReLU(inplace)
    (39): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (42): ReLU(inplace)
    (43): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (44): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (45): ReLU(inplace)
    (46): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (47): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (48): ReLU(inplace)
    (49): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (50): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (51): ReLU(inplace)
    (52): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (classifier): Sequential(
    (0): Linear(in_features=25088, out_features=4096, bias=True)
    (1): ReLU()
    (2): Dropout(p=0.2)
    (3): Linear(in_features=4096, out_features=1024, bias=True)
    (4): ReLU()
    (5): Dropout(p=0.2)
    (6): Linear(in_features=1024, out_features=102, bias=True)
    (7): LogSoftmax()
  )
)
In [15]:
"""
Code adapted from Udacity Deep Learning with Pytorch lesson
"""

epochs = 7
steps = 0
running_loss = 0
print_every = 5

with active_session():

    for epoch in range(epochs):
        for inputs, labels in trainloader:
            steps += 1
            # Move input and label tensors to the default device
            inputs, labels = inputs.to(device), labels.to(device)

            optimizer.zero_grad()

            logps = model.forward(inputs)
            loss = criterion(logps, labels)
            loss.backward()
            optimizer.step()

            running_loss += loss.item()

            if steps % print_every == 0:
                valid_loss = 0
                accuracy = 0
                model.eval()
                with torch.no_grad():
                    for inputs, labels in validloader:
                        inputs, labels = inputs.to(device), labels.to(device)
                        logps = model.forward(inputs)
                        batch_loss = criterion(logps, labels)

                        valid_loss += batch_loss.item()

                        # Calculate accuracy
                        ps = torch.exp(logps)
                        top_p, top_class = ps.topk(1, dim=1)
                        equals = top_class == labels.view(*top_class.shape)
                        accuracy += torch.mean(equals.type(torch.FloatTensor)).item()

                print(f"Epoch {epoch+1}/{epochs}.. "
                      f"Train loss: {running_loss/print_every:.3f}.. "
                      f"Validation loss: {valid_loss/len(validloader):.3f}.. "
                      f"Validation accuracy: {accuracy/len(validloader):.3f}")
                running_loss = 0
                model.train()
Epoch 1/7.. Train loss: 4.806.. Validation loss: 4.552.. Validation accuracy: 0.066
Epoch 1/7.. Train loss: 4.574.. Validation loss: 4.420.. Validation accuracy: 0.062
Epoch 1/7.. Train loss: 4.405.. Validation loss: 4.279.. Validation accuracy: 0.089
Epoch 1/7.. Train loss: 4.247.. Validation loss: 4.016.. Validation accuracy: 0.129
Epoch 1/7.. Train loss: 4.095.. Validation loss: 3.775.. Validation accuracy: 0.179
Epoch 1/7.. Train loss: 3.801.. Validation loss: 3.478.. Validation accuracy: 0.219
Epoch 1/7.. Train loss: 3.609.. Validation loss: 3.224.. Validation accuracy: 0.266
Epoch 1/7.. Train loss: 3.270.. Validation loss: 2.835.. Validation accuracy: 0.321
Epoch 1/7.. Train loss: 3.119.. Validation loss: 2.577.. Validation accuracy: 0.391
Epoch 1/7.. Train loss: 3.022.. Validation loss: 2.402.. Validation accuracy: 0.411
Epoch 1/7.. Train loss: 2.634.. Validation loss: 2.028.. Validation accuracy: 0.476
Epoch 1/7.. Train loss: 2.345.. Validation loss: 1.783.. Validation accuracy: 0.528
Epoch 1/7.. Train loss: 2.285.. Validation loss: 1.721.. Validation accuracy: 0.531
Epoch 1/7.. Train loss: 2.325.. Validation loss: 1.573.. Validation accuracy: 0.563
Epoch 1/7.. Train loss: 2.211.. Validation loss: 1.499.. Validation accuracy: 0.587
Epoch 1/7.. Train loss: 2.066.. Validation loss: 1.495.. Validation accuracy: 0.579
Epoch 1/7.. Train loss: 2.016.. Validation loss: 1.421.. Validation accuracy: 0.611
Epoch 1/7.. Train loss: 2.134.. Validation loss: 1.338.. Validation accuracy: 0.632
Epoch 1/7.. Train loss: 1.725.. Validation loss: 1.242.. Validation accuracy: 0.646
Epoch 1/7.. Train loss: 1.639.. Validation loss: 1.171.. Validation accuracy: 0.649
Epoch 2/7.. Train loss: 1.656.. Validation loss: 1.076.. Validation accuracy: 0.680
Epoch 2/7.. Train loss: 1.710.. Validation loss: 1.068.. Validation accuracy: 0.686
Epoch 2/7.. Train loss: 1.502.. Validation loss: 1.018.. Validation accuracy: 0.707
Epoch 2/7.. Train loss: 1.337.. Validation loss: 0.993.. Validation accuracy: 0.716
Epoch 2/7.. Train loss: 1.353.. Validation loss: 0.877.. Validation accuracy: 0.731
Epoch 2/7.. Train loss: 1.349.. Validation loss: 0.947.. Validation accuracy: 0.728
Epoch 2/7.. Train loss: 1.424.. Validation loss: 0.888.. Validation accuracy: 0.753
Epoch 2/7.. Train loss: 1.419.. Validation loss: 0.869.. Validation accuracy: 0.752
Epoch 2/7.. Train loss: 1.367.. Validation loss: 0.865.. Validation accuracy: 0.761
Epoch 2/7.. Train loss: 1.368.. Validation loss: 0.904.. Validation accuracy: 0.723
Epoch 2/7.. Train loss: 1.496.. Validation loss: 0.834.. Validation accuracy: 0.754
Epoch 2/7.. Train loss: 1.388.. Validation loss: 0.894.. Validation accuracy: 0.738
Epoch 2/7.. Train loss: 1.389.. Validation loss: 0.833.. Validation accuracy: 0.741
Epoch 2/7.. Train loss: 1.251.. Validation loss: 0.806.. Validation accuracy: 0.787
Epoch 2/7.. Train loss: 1.326.. Validation loss: 0.731.. Validation accuracy: 0.802
Epoch 2/7.. Train loss: 1.276.. Validation loss: 0.646.. Validation accuracy: 0.811
Epoch 2/7.. Train loss: 1.147.. Validation loss: 0.636.. Validation accuracy: 0.832
Epoch 2/7.. Train loss: 1.198.. Validation loss: 0.651.. Validation accuracy: 0.821
Epoch 2/7.. Train loss: 1.191.. Validation loss: 0.667.. Validation accuracy: 0.809
Epoch 2/7.. Train loss: 1.110.. Validation loss: 0.693.. Validation accuracy: 0.797
Epoch 2/7.. Train loss: 1.324.. Validation loss: 0.637.. Validation accuracy: 0.820
Epoch 3/7.. Train loss: 1.232.. Validation loss: 0.627.. Validation accuracy: 0.829
Epoch 3/7.. Train loss: 1.053.. Validation loss: 0.670.. Validation accuracy: 0.807
Epoch 3/7.. Train loss: 1.068.. Validation loss: 0.597.. Validation accuracy: 0.837
Epoch 3/7.. Train loss: 1.104.. Validation loss: 0.596.. Validation accuracy: 0.828
Epoch 3/7.. Train loss: 1.030.. Validation loss: 0.566.. Validation accuracy: 0.842
Epoch 3/7.. Train loss: 1.064.. Validation loss: 0.516.. Validation accuracy: 0.859
Epoch 3/7.. Train loss: 1.113.. Validation loss: 0.549.. Validation accuracy: 0.839
Epoch 3/7.. Train loss: 0.944.. Validation loss: 0.593.. Validation accuracy: 0.835
Epoch 3/7.. Train loss: 0.970.. Validation loss: 0.573.. Validation accuracy: 0.825
Epoch 3/7.. Train loss: 1.053.. Validation loss: 0.475.. Validation accuracy: 0.863
Epoch 3/7.. Train loss: 0.889.. Validation loss: 0.484.. Validation accuracy: 0.859
Epoch 3/7.. Train loss: 0.999.. Validation loss: 0.502.. Validation accuracy: 0.862
Epoch 3/7.. Train loss: 1.006.. Validation loss: 0.557.. Validation accuracy: 0.830
Epoch 3/7.. Train loss: 0.979.. Validation loss: 0.528.. Validation accuracy: 0.840
Epoch 3/7.. Train loss: 0.998.. Validation loss: 0.548.. Validation accuracy: 0.846
Epoch 3/7.. Train loss: 1.000.. Validation loss: 0.537.. Validation accuracy: 0.842
Epoch 3/7.. Train loss: 1.009.. Validation loss: 0.491.. Validation accuracy: 0.863
Epoch 3/7.. Train loss: 1.005.. Validation loss: 0.546.. Validation accuracy: 0.847
Epoch 3/7.. Train loss: 0.978.. Validation loss: 0.513.. Validation accuracy: 0.846
Epoch 3/7.. Train loss: 0.784.. Validation loss: 0.510.. Validation accuracy: 0.857
Epoch 4/7.. Train loss: 0.930.. Validation loss: 0.508.. Validation accuracy: 0.848
Epoch 4/7.. Train loss: 0.887.. Validation loss: 0.497.. Validation accuracy: 0.847
Epoch 4/7.. Train loss: 0.837.. Validation loss: 0.537.. Validation accuracy: 0.844
Epoch 4/7.. Train loss: 0.926.. Validation loss: 0.496.. Validation accuracy: 0.859
Epoch 4/7.. Train loss: 0.965.. Validation loss: 0.473.. Validation accuracy: 0.870
Epoch 4/7.. Train loss: 0.840.. Validation loss: 0.451.. Validation accuracy: 0.874
Epoch 4/7.. Train loss: 0.983.. Validation loss: 0.461.. Validation accuracy: 0.872
Epoch 4/7.. Train loss: 0.822.. Validation loss: 0.497.. Validation accuracy: 0.852
Epoch 4/7.. Train loss: 0.921.. Validation loss: 0.452.. Validation accuracy: 0.870
Epoch 4/7.. Train loss: 0.898.. Validation loss: 0.429.. Validation accuracy: 0.883
Epoch 4/7.. Train loss: 0.904.. Validation loss: 0.480.. Validation accuracy: 0.859
Epoch 4/7.. Train loss: 0.924.. Validation loss: 0.460.. Validation accuracy: 0.875
Epoch 4/7.. Train loss: 0.802.. Validation loss: 0.446.. Validation accuracy: 0.864
Epoch 4/7.. Train loss: 0.662.. Validation loss: 0.456.. Validation accuracy: 0.878
Epoch 4/7.. Train loss: 0.777.. Validation loss: 0.422.. Validation accuracy: 0.879
Epoch 4/7.. Train loss: 0.829.. Validation loss: 0.464.. Validation accuracy: 0.860
Epoch 4/7.. Train loss: 0.959.. Validation loss: 0.455.. Validation accuracy: 0.866
Epoch 4/7.. Train loss: 0.807.. Validation loss: 0.523.. Validation accuracy: 0.843
Epoch 4/7.. Train loss: 0.839.. Validation loss: 0.441.. Validation accuracy: 0.863
Epoch 4/7.. Train loss: 0.882.. Validation loss: 0.467.. Validation accuracy: 0.853
Epoch 4/7.. Train loss: 0.917.. Validation loss: 0.425.. Validation accuracy: 0.884
Epoch 5/7.. Train loss: 0.801.. Validation loss: 0.428.. Validation accuracy: 0.875
Epoch 5/7.. Train loss: 0.955.. Validation loss: 0.471.. Validation accuracy: 0.864
Epoch 5/7.. Train loss: 0.844.. Validation loss: 0.541.. Validation accuracy: 0.856
Epoch 5/7.. Train loss: 0.834.. Validation loss: 0.559.. Validation accuracy: 0.836
Epoch 5/7.. Train loss: 0.850.. Validation loss: 0.419.. Validation accuracy: 0.886
Epoch 5/7.. Train loss: 0.664.. Validation loss: 0.433.. Validation accuracy: 0.871
Epoch 5/7.. Train loss: 0.846.. Validation loss: 0.472.. Validation accuracy: 0.871
Epoch 5/7.. Train loss: 0.739.. Validation loss: 0.412.. Validation accuracy: 0.883
Epoch 5/7.. Train loss: 0.694.. Validation loss: 0.395.. Validation accuracy: 0.888
Epoch 5/7.. Train loss: 0.823.. Validation loss: 0.387.. Validation accuracy: 0.904
Epoch 5/7.. Train loss: 0.717.. Validation loss: 0.406.. Validation accuracy: 0.882
Epoch 5/7.. Train loss: 0.909.. Validation loss: 0.427.. Validation accuracy: 0.877
Epoch 5/7.. Train loss: 0.740.. Validation loss: 0.418.. Validation accuracy: 0.867
Epoch 5/7.. Train loss: 0.771.. Validation loss: 0.384.. Validation accuracy: 0.883
Epoch 5/7.. Train loss: 0.864.. Validation loss: 0.395.. Validation accuracy: 0.887
Epoch 5/7.. Train loss: 0.623.. Validation loss: 0.505.. Validation accuracy: 0.854
Epoch 5/7.. Train loss: 0.766.. Validation loss: 0.433.. Validation accuracy: 0.878
Epoch 5/7.. Train loss: 0.899.. Validation loss: 0.380.. Validation accuracy: 0.890
Epoch 5/7.. Train loss: 0.826.. Validation loss: 0.413.. Validation accuracy: 0.885
Epoch 5/7.. Train loss: 0.783.. Validation loss: 0.427.. Validation accuracy: 0.885
Epoch 5/7.. Train loss: 0.828.. Validation loss: 0.444.. Validation accuracy: 0.868
Epoch 6/7.. Train loss: 0.743.. Validation loss: 0.427.. Validation accuracy: 0.869
Epoch 6/7.. Train loss: 0.783.. Validation loss: 0.401.. Validation accuracy: 0.880
Epoch 6/7.. Train loss: 0.688.. Validation loss: 0.411.. Validation accuracy: 0.875
Epoch 6/7.. Train loss: 0.756.. Validation loss: 0.443.. Validation accuracy: 0.872
Epoch 6/7.. Train loss: 0.706.. Validation loss: 0.376.. Validation accuracy: 0.886
Epoch 6/7.. Train loss: 0.830.. Validation loss: 0.395.. Validation accuracy: 0.899
Epoch 6/7.. Train loss: 0.834.. Validation loss: 0.417.. Validation accuracy: 0.880
Epoch 6/7.. Train loss: 0.812.. Validation loss: 0.460.. Validation accuracy: 0.860
Epoch 6/7.. Train loss: 0.748.. Validation loss: 0.348.. Validation accuracy: 0.901
Epoch 6/7.. Train loss: 0.600.. Validation loss: 0.352.. Validation accuracy: 0.903
Epoch 6/7.. Train loss: 0.650.. Validation loss: 0.359.. Validation accuracy: 0.900
Epoch 6/7.. Train loss: 0.666.. Validation loss: 0.404.. Validation accuracy: 0.892
Epoch 6/7.. Train loss: 0.648.. Validation loss: 0.407.. Validation accuracy: 0.889
Epoch 6/7.. Train loss: 0.814.. Validation loss: 0.366.. Validation accuracy: 0.902
Epoch 6/7.. Train loss: 0.666.. Validation loss: 0.387.. Validation accuracy: 0.884
Epoch 6/7.. Train loss: 0.822.. Validation loss: 0.429.. Validation accuracy: 0.883
Epoch 6/7.. Train loss: 0.850.. Validation loss: 0.385.. Validation accuracy: 0.887
Epoch 6/7.. Train loss: 0.762.. Validation loss: 0.382.. Validation accuracy: 0.884
Epoch 6/7.. Train loss: 0.631.. Validation loss: 0.381.. Validation accuracy: 0.887
Epoch 6/7.. Train loss: 0.660.. Validation loss: 0.398.. Validation accuracy: 0.881
Epoch 7/7.. Train loss: 0.695.. Validation loss: 0.455.. Validation accuracy: 0.873
Epoch 7/7.. Train loss: 0.665.. Validation loss: 0.437.. Validation accuracy: 0.873
Epoch 7/7.. Train loss: 0.625.. Validation loss: 0.354.. Validation accuracy: 0.905
Epoch 7/7.. Train loss: 0.784.. Validation loss: 0.390.. Validation accuracy: 0.883
Epoch 7/7.. Train loss: 0.603.. Validation loss: 0.360.. Validation accuracy: 0.896
Epoch 7/7.. Train loss: 0.781.. Validation loss: 0.364.. Validation accuracy: 0.907
Epoch 7/7.. Train loss: 0.705.. Validation loss: 0.376.. Validation accuracy: 0.899
Epoch 7/7.. Train loss: 0.704.. Validation loss: 0.378.. Validation accuracy: 0.904
Epoch 7/7.. Train loss: 0.638.. Validation loss: 0.374.. Validation accuracy: 0.890
Epoch 7/7.. Train loss: 0.644.. Validation loss: 0.364.. Validation accuracy: 0.903
Epoch 7/7.. Train loss: 0.637.. Validation loss: 0.401.. Validation accuracy: 0.876
Epoch 7/7.. Train loss: 0.609.. Validation loss: 0.374.. Validation accuracy: 0.894
Epoch 7/7.. Train loss: 0.664.. Validation loss: 0.351.. Validation accuracy: 0.904
Epoch 7/7.. Train loss: 0.756.. Validation loss: 0.382.. Validation accuracy: 0.896
Epoch 7/7.. Train loss: 0.694.. Validation loss: 0.412.. Validation accuracy: 0.883
Epoch 7/7.. Train loss: 0.753.. Validation loss: 0.366.. Validation accuracy: 0.903
Epoch 7/7.. Train loss: 0.543.. Validation loss: 0.374.. Validation accuracy: 0.899
Epoch 7/7.. Train loss: 0.748.. Validation loss: 0.403.. Validation accuracy: 0.884
Epoch 7/7.. Train loss: 0.697.. Validation loss: 0.385.. Validation accuracy: 0.897
Epoch 7/7.. Train loss: 0.624.. Validation loss: 0.386.. Validation accuracy: 0.882
Epoch 7/7.. Train loss: 0.663.. Validation loss: 0.359.. Validation accuracy: 0.896

accuracy on the testing set more or less platoes after epoch 5

Testing your network

It's good practice to test your trained network on test data, images the network has never seen either in training or validation. This will give you a good estimate for the model's performance on completely new images. Run the test images through the network and measure the accuracy, the same way you did validation. You should be able to reach around 70% accuracy on the test set if the model has been trained well.

In [16]:
# TODO: Do validation on the test set
"""
Source: Udacity Deep Learning with Pytorch lesson
"""
def test_Model():
    inputs, labels = next(iter(testloader))
    inputs, labels = inputs.to(device), labels.to(device)

    # Get the class probabilities
    ps = torch.exp(model(inputs))

    # Make sure the shape is appropriate, we should get 102 class probabilities for 64 examples
    print(ps.shape)

    top_p, top_class = ps.topk(1, dim=1)
    # Look at the most likely classes for the first 10 examples
    print(top_class[:10,:])

    equals = top_class == labels.view(*top_class.shape)

    accuracy = torch.mean(equals.type(torch.FloatTensor))
    print(f'Accuracy: {accuracy.item()*100}%')
In [19]:
test_Model()
torch.Size([64, 102])
tensor([[  72],
        [   8],
        [  89],
        [  61],
        [  73],
        [ 100],
        [  24],
        [  62],
        [  92],
        [  81]], device='cuda:0')
Accuracy: 92.1875%

Save the checkpoint

Now that your network is trained, save the model so you can load it later for making predictions. You probably want to save other things such as the mapping of classes to indices which you get from one of the image datasets: image_datasets['train'].class_to_idx. You can attach this to the model as an attribute which makes inference easier later on.

model.class_to_idx = image_datasets['train'].class_to_idx

Remember that you'll want to completely rebuild the model later so you can use it for inference. Make sure to include any information you need in the checkpoint. If you want to load the model and keep training, you'll want to save the number of epochs as well as the optimizer state, optimizer.state_dict. You'll likely want to use this trained model in the next part of the project, so best to save it now.

In [23]:
model.class_to_idx = train_data.class_to_idx 
In [24]:
model.class_to_idx
Out[24]:
{'1': 0,
 '10': 1,
 '100': 2,
 '101': 3,
 '102': 4,
 '11': 5,
 '12': 6,
 '13': 7,
 '14': 8,
 '15': 9,
 '16': 10,
 '17': 11,
 '18': 12,
 '19': 13,
 '2': 14,
 '20': 15,
 '21': 16,
 '22': 17,
 '23': 18,
 '24': 19,
 '25': 20,
 '26': 21,
 '27': 22,
 '28': 23,
 '29': 24,
 '3': 25,
 '30': 26,
 '31': 27,
 '32': 28,
 '33': 29,
 '34': 30,
 '35': 31,
 '36': 32,
 '37': 33,
 '38': 34,
 '39': 35,
 '4': 36,
 '40': 37,
 '41': 38,
 '42': 39,
 '43': 40,
 '44': 41,
 '45': 42,
 '46': 43,
 '47': 44,
 '48': 45,
 '49': 46,
 '5': 47,
 '50': 48,
 '51': 49,
 '52': 50,
 '53': 51,
 '54': 52,
 '55': 53,
 '56': 54,
 '57': 55,
 '58': 56,
 '59': 57,
 '6': 58,
 '60': 59,
 '61': 60,
 '62': 61,
 '63': 62,
 '64': 63,
 '65': 64,
 '66': 65,
 '67': 66,
 '68': 67,
 '69': 68,
 '7': 69,
 '70': 70,
 '71': 71,
 '72': 72,
 '73': 73,
 '74': 74,
 '75': 75,
 '76': 76,
 '77': 77,
 '78': 78,
 '79': 79,
 '8': 80,
 '80': 81,
 '81': 82,
 '82': 83,
 '83': 84,
 '84': 85,
 '85': 86,
 '86': 87,
 '87': 88,
 '88': 89,
 '89': 90,
 '9': 91,
 '90': 92,
 '91': 93,
 '92': 94,
 '93': 95,
 '94': 96,
 '95': 97,
 '96': 98,
 '97': 99,
 '98': 100,
 '99': 101}
In [20]:
print("Our model: \n\n", model, '\n')
print("The state dict keys: \n\n", model.state_dict().keys())
Our model: 

 VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace)
    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU(inplace)
    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (9): ReLU(inplace)
    (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (12): ReLU(inplace)
    (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (16): ReLU(inplace)
    (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (19): ReLU(inplace)
    (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (22): ReLU(inplace)
    (23): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (24): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (25): ReLU(inplace)
    (26): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (27): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (29): ReLU(inplace)
    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (32): ReLU(inplace)
    (33): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (34): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (35): ReLU(inplace)
    (36): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (37): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (38): ReLU(inplace)
    (39): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (42): ReLU(inplace)
    (43): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (44): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (45): ReLU(inplace)
    (46): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (47): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (48): ReLU(inplace)
    (49): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (50): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (51): ReLU(inplace)
    (52): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (classifier): Sequential(
    (0): Linear(in_features=25088, out_features=4096, bias=True)
    (1): ReLU()
    (2): Dropout(p=0.2)
    (3): Linear(in_features=4096, out_features=1024, bias=True)
    (4): ReLU()
    (5): Dropout(p=0.2)
    (6): Linear(in_features=1024, out_features=102, bias=True)
    (7): LogSoftmax()
  )
) 

The state dict keys: 

 odict_keys(['features.0.weight', 'features.0.bias', 'features.1.weight', 'features.1.bias', 'features.1.running_mean', 'features.1.running_var', 'features.3.weight', 'features.3.bias', 'features.4.weight', 'features.4.bias', 'features.4.running_mean', 'features.4.running_var', 'features.7.weight', 'features.7.bias', 'features.8.weight', 'features.8.bias', 'features.8.running_mean', 'features.8.running_var', 'features.10.weight', 'features.10.bias', 'features.11.weight', 'features.11.bias', 'features.11.running_mean', 'features.11.running_var', 'features.14.weight', 'features.14.bias', 'features.15.weight', 'features.15.bias', 'features.15.running_mean', 'features.15.running_var', 'features.17.weight', 'features.17.bias', 'features.18.weight', 'features.18.bias', 'features.18.running_mean', 'features.18.running_var', 'features.20.weight', 'features.20.bias', 'features.21.weight', 'features.21.bias', 'features.21.running_mean', 'features.21.running_var', 'features.23.weight', 'features.23.bias', 'features.24.weight', 'features.24.bias', 'features.24.running_mean', 'features.24.running_var', 'features.27.weight', 'features.27.bias', 'features.28.weight', 'features.28.bias', 'features.28.running_mean', 'features.28.running_var', 'features.30.weight', 'features.30.bias', 'features.31.weight', 'features.31.bias', 'features.31.running_mean', 'features.31.running_var', 'features.33.weight', 'features.33.bias', 'features.34.weight', 'features.34.bias', 'features.34.running_mean', 'features.34.running_var', 'features.36.weight', 'features.36.bias', 'features.37.weight', 'features.37.bias', 'features.37.running_mean', 'features.37.running_var', 'features.40.weight', 'features.40.bias', 'features.41.weight', 'features.41.bias', 'features.41.running_mean', 'features.41.running_var', 'features.43.weight', 'features.43.bias', 'features.44.weight', 'features.44.bias', 'features.44.running_mean', 'features.44.running_var', 'features.46.weight', 'features.46.bias', 'features.47.weight', 'features.47.bias', 'features.47.running_mean', 'features.47.running_var', 'features.49.weight', 'features.49.bias', 'features.50.weight', 'features.50.bias', 'features.50.running_mean', 'features.50.running_var', 'classifier.0.weight', 'classifier.0.bias', 'classifier.3.weight', 'classifier.3.bias', 'classifier.6.weight', 'classifier.6.bias'])
In [21]:
model.state_dict ()
Out[21]:
OrderedDict([('features.0.weight',
              tensor([[[[-3.2130e-08, -3.1717e-08, -6.7120e-08],
                        [-2.9321e-08, -2.1596e-08, -5.3207e-08],
                        [-5.3304e-08, -4.0832e-08, -6.6434e-08]],
              
                       [[ 1.3710e-07,  1.2848e-07,  7.1270e-08],
                        [ 1.5487e-07,  1.5186e-07,  8.3582e-08],
                        [ 9.3392e-08,  9.9451e-08,  4.1284e-08]],
              
                       [[ 1.7435e-07,  1.7840e-07,  1.0561e-07],
                        [ 1.9644e-07,  1.9769e-07,  1.2560e-07],
                        [ 1.1935e-07,  1.3447e-07,  5.9638e-08]]],
              
              
                      [[[-6.3467e-08, -5.2714e-08, -3.8511e-08],
                        [-1.4349e-08, -9.0426e-09, -1.7164e-08],
                        [ 2.2282e-08,  1.9084e-08,  1.8150e-09]],
              
                       [[-1.2191e-08, -6.1933e-09,  8.5022e-09],
                        [ 3.8618e-08,  5.8869e-08,  2.2082e-08],
                        [ 6.2501e-08,  7.0528e-08,  2.7185e-08]],
              
                       [[ 3.0045e-08,  3.8003e-08,  3.5389e-08],
                        [ 8.6743e-08,  1.1626e-07,  8.0502e-08],
                        [ 7.9653e-08,  1.0057e-07,  6.7894e-08]]],
              
              
                      [[[-1.3565e-01, -2.9960e-01, -1.5151e-01],
                        [-2.6804e-01, -3.0505e-01, -9.4600e-02],
                        [-1.4124e-01, -8.1448e-02,  1.7112e-01]],
              
                       [[ 2.0333e-01,  4.0123e-01,  1.3747e-01],
                        [ 4.2157e-01,  6.7595e-01,  3.2725e-01],
                        [ 1.3103e-01,  3.2654e-01,  6.4284e-02]],
              
                       [[-4.1092e-02,  4.6723e-02, -9.7236e-03],
                        [ 2.9558e-02,  3.5226e-02, -1.2338e-01],
                        [-9.1504e-02, -1.7836e-01, -3.1635e-01]]],
              
              
                      ...,
              
              
                      [[[-5.0743e-08, -6.6759e-08, -7.8941e-08],
                        [-4.6060e-08, -7.9228e-08, -7.3020e-08],
                        [-7.2354e-08, -7.5888e-08, -7.5602e-08]],
              
                       [[-1.8643e-08, -5.4891e-08, -5.5029e-08],
                        [-2.9716e-08, -6.0283e-08, -5.9039e-08],
                        [-5.4538e-08, -5.7847e-08, -5.4300e-08]],
              
                       [[-9.0202e-09, -4.0853e-08, -4.0702e-08],
                        [-2.0145e-08, -4.0434e-08, -4.7582e-08],
                        [-4.9317e-08, -4.9057e-08, -4.8955e-08]]],
              
              
                      [[[-7.8860e-09,  4.4172e-09,  4.1371e-08],
                        [-9.1843e-09,  4.9512e-09,  6.3214e-08],
                        [ 1.2759e-09,  2.6705e-08,  6.1890e-08]],
              
                       [[-1.6242e-09, -5.1753e-10,  3.5820e-08],
                        [-1.5325e-08,  9.5484e-09,  6.6321e-08],
                        [ 5.8824e-09,  3.6904e-08,  7.9135e-08]],
              
                       [[ 7.1371e-09,  3.5890e-09,  2.7016e-08],
                        [-2.0624e-09,  1.1004e-08,  5.9306e-08],
                        [ 2.0382e-08,  3.8491e-08,  6.5097e-08]]],
              
              
                      [[[-5.1288e-07,  4.4387e-06,  2.8007e-06],
                        [ 1.4936e-06,  6.4549e-06,  4.6987e-06],
                        [ 1.3737e-06,  3.8875e-06,  3.2473e-06]],
              
                       [[-2.3703e-06,  3.5919e-06,  1.5149e-06],
                        [ 6.3111e-07,  6.6647e-06,  4.2479e-06],
                        [-2.4147e-07,  2.5924e-06,  1.3272e-06]],
              
                       [[ 4.0485e-06,  1.1047e-05,  7.5509e-06],
                        [ 7.4615e-06,  1.4459e-05,  1.0637e-05],
                        [ 4.2098e-06,  7.7080e-06,  5.1014e-06]]]], device='cuda:0')),
             ('features.0.bias',
              tensor([ 1.6107e-12,  2.3927e-13,  8.1896e-07, -5.4667e-07,  2.2830e-06,
                      -5.2139e-12, -2.7520e-07, -1.2108e-05, -2.4546e-07,  6.1401e-13,
                      -7.4896e-13,  9.2633e-13, -1.0190e-13, -1.4083e-11,  3.2897e-12,
                       3.3589e-06, -3.8043e-12, -4.6628e-07, -4.6298e-12, -1.1104e-12,
                      -1.9605e-10, -2.1974e-12, -1.3157e-07,  1.6526e-12,  1.0491e-13,
                      -2.0267e-13,  1.8571e-12, -1.4813e-13, -3.0566e-13, -5.9317e-13,
                      -4.3687e-13,  1.5774e-11, -1.6906e-12,  9.4310e-16, -9.0617e-07,
                      -1.1036e-06,  1.2412e-10,  2.8499e-06, -2.4069e-13,  1.5473e-09,
                      -8.5165e-15, -1.4024e-09,  3.6829e-12, -5.4786e-13, -8.1585e-12,
                       6.2113e-12,  1.2291e-13,  6.3706e-14, -3.0080e-14, -2.4810e-13,
                      -1.2556e-06, -9.8116e-13,  1.1568e-06, -2.3078e-06,  1.2771e-07,
                      -4.9246e-13,  7.9006e-08, -9.7580e-10, -1.0113e-06,  2.6458e-06,
                       5.2448e-12, -7.0300e-13,  2.7669e-13, -1.0255e-10], device='cuda:0')),
             ('features.1.weight',
              tensor([ 1.5763e-07,  9.8841e-08,  2.4442e-01,  2.2678e-01,  2.4756e-01,
                       5.9402e-07,  1.5344e-01,  5.5348e-01,  1.8775e-01,  4.0541e-08,
                       7.3613e-08,  6.7899e-07,  1.9412e-08,  3.7765e-07, -1.5930e-07,
                       4.6782e-01, -5.3255e-07,  1.8139e-02,  1.9039e-08,  2.0468e-07,
                      -3.8896e-05,  8.2928e-07,  3.6763e-01, -5.7934e-08,  1.0634e-07,
                       7.7692e-09, -1.2172e-07,  1.0209e-07,  1.0130e-07,  3.9670e-08,
                       4.9515e-08,  6.8137e-07,  1.0767e-07,  6.3167e-08,  5.1813e-01,
                       3.9662e-01,  4.0801e-06,  3.2676e-01,  3.0591e-08,  1.4233e-05,
                      -4.0759e-07,  2.1676e-04,  1.8606e-06,  2.0022e-08,  6.8042e-07,
                       1.0519e-06,  3.0301e-07,  1.7505e-08,  1.4339e-07,  2.6455e-08,
                       3.7188e-01, -1.0622e-06,  1.9643e-01,  6.8300e-01,  1.8162e-01,
                      -1.1994e-07,  3.4379e-01,  1.2878e-04,  6.3663e-01,  6.3070e-01,
                       2.0438e-07,  1.0555e-08,  1.4975e-08,  3.4700e-06], device='cuda:0')),
             ('features.1.bias',
              tensor([-4.8111e-07, -3.2566e-07,  5.0622e-01,  6.4461e-01, -8.4517e-02,
                      -2.7397e-06,  5.1349e-01,  4.1654e-01,  5.9912e-01, -3.1594e-07,
                      -2.2759e-07, -2.0965e-06, -4.1617e-08, -2.7742e-06, -6.4659e-07,
                      -4.6228e-01, -1.5745e-06, -1.2689e-01, -4.6075e-07, -9.2042e-07,
                      -1.8860e-04, -1.9513e-06,  2.3087e-01, -7.7523e-07, -2.9689e-07,
                      -5.4331e-08, -3.5619e-07, -3.6039e-07, -2.4188e-07, -1.1089e-07,
                      -1.5956e-07, -3.9949e-06, -3.2496e-07, -8.0513e-07,  3.7280e-01,
                       2.2304e-01, -1.7163e-05, -3.9715e-01, -9.0987e-08, -6.0867e-05,
                      -1.0449e-06, -6.3772e-04, -5.4870e-06, -9.0156e-08, -1.9893e-06,
                      -3.2460e-06, -9.5805e-07, -7.0729e-08, -4.5174e-07, -6.1239e-08,
                       2.0177e-01, -4.1421e-06,  4.4383e-01, -6.5162e-01,  3.9662e-01,
                      -1.3882e-06,  1.9245e-01, -4.1191e-04, -5.7675e-01, -6.7851e-01,
                      -1.0461e-06, -3.5752e-08, -6.6529e-08, -5.1440e-05], device='cuda:0')),
             ('features.1.running_mean',
              tensor([-7.0231e-07, -2.9656e-07, -5.1152e-01,  7.2378e-01,  2.8782e-01,
                      -2.7657e-06,  6.3153e-01,  1.9065e-02,  1.3975e-01,  4.7056e-08,
                      -5.0858e-07, -9.4565e-07,  1.4386e-07, -2.2057e-06,  1.7344e-06,
                       5.3788e-02, -1.1926e-06,  1.7925e-02, -2.0122e-08, -3.4827e-07,
                       1.3092e-04,  7.9233e-06, -1.2169e-02, -2.1120e-07, -1.3809e-07,
                      -1.0263e-07,  2.7212e-06, -1.7831e-07,  1.1626e-07, -9.9240e-08,
                      -2.4407e-08,  3.8247e-06, -1.9504e-07,  5.2103e-07, -8.4754e-06,
                      -1.0387e-02,  4.2505e-06, -1.4466e-01, -1.1122e-07, -3.4743e-05,
                      -1.3765e-06, -2.6676e-03, -5.3828e-07, -1.0498e-07, -1.2645e-06,
                       1.1436e-06, -3.1558e-06, -1.6910e-07,  1.0879e-07, -1.1742e-08,
                      -8.8959e-03, -1.1962e-07,  1.8312e-03,  1.6341e-01, -1.0706e-01,
                       5.9130e-07,  7.0088e-03, -5.4878e-04,  3.6713e-01, -2.9605e-02,
                       8.4384e-07,  1.4787e-07, -1.0586e-07, -2.5905e-05], device='cuda:0')),
             ('features.1.running_var',
              tensor([ 4.4663e-12,  8.0464e-13,  4.0769e+00,  7.1585e+00,  7.3639e-01,
                       4.8297e-11,  3.1076e+00,  8.4893e-02,  4.3034e+00,  1.6702e-12,
                       8.0861e-12,  5.9497e-11,  3.8648e-13,  8.3875e-11,  3.2074e-11,
                       1.1559e+00,  1.2590e-11,  2.5434e-02,  1.1524e-13,  7.7499e-13,
                       1.1224e-07,  2.6225e-09,  2.1006e+00,  6.7933e-12,  3.2296e-13,
                       7.6610e-14,  4.7029e-10,  6.3205e-13,  3.6169e-13,  5.2312e-13,
                       5.3040e-13,  1.7222e-10,  5.6044e-13,  4.6840e-11,  8.9726e-02,
                       2.6741e+00,  7.6734e-10,  2.4625e-01,  7.3220e-13,  3.8951e-08,
                       1.6453e-11,  4.4593e-04,  1.2758e-10,  1.6363e-13,  9.2440e-11,
                       1.4374e-10,  3.9434e-10,  3.8943e-13,  1.4741e-12,  1.3093e-12,
                       2.3012e+00,  3.3229e-10,  7.6995e-02,  1.0164e+00,  4.1746e+00,
                       2.5172e-11,  1.8304e+00,  2.4451e-06,  1.7403e+00,  9.5213e-01,
                       1.0402e-11,  2.2174e-12,  4.3912e-13,  1.6671e-08], device='cuda:0')),
             ('features.3.weight',
              tensor([[[[ 6.2157e-09,  1.0084e-08,  1.5116e-08],
                        [ 4.5769e-09,  2.0736e-08,  1.7261e-08],
                        [ 5.1316e-09,  1.1234e-08,  1.7994e-08]],
              
                       [[ 1.0657e-08,  2.5749e-08,  2.5256e-08],
                        [ 7.2024e-09,  8.4045e-09,  1.7773e-08],
                        [-1.1012e-08, -8.1895e-10,  3.7367e-09]],
              
                       [[-1.3840e-05, -1.3677e-05, -1.6122e-05],
                        [-9.1985e-06, -8.5148e-06, -1.0859e-05],
                        [-8.0684e-06, -7.3427e-06, -9.3098e-06]],
              
                       ...,
              
                       [[-8.5300e-09, -7.7074e-09, -2.0645e-09],
                        [-6.2058e-09, -5.2973e-09, -3.6659e-09],
                        [-1.5674e-08, -3.6743e-09, -9.8995e-09]],
              
                       [[-3.6941e-09, -5.3672e-09,  1.2915e-09],
                        [-3.1108e-09, -5.9326e-10,  1.8409e-09],
                        [ 2.0104e-09, -6.4832e-09, -1.1158e-08]],
              
                       [[ 2.5512e-06,  2.7464e-06,  1.8505e-06],
                        [ 3.8421e-06,  4.2444e-06,  3.2752e-06],
                        [ 3.5921e-06,  4.1304e-06,  3.3315e-06]]],
              
              
                      [[[ 6.6645e-09,  8.9393e-10, -2.2275e-08],
                        [ 2.5102e-08,  1.1624e-08, -2.0613e-08],
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                       [[ 3.0317e-09,  2.5751e-10, -1.7066e-08],
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                       0.0229,  0.0225,  0.0161,  0.0336,  0.0190,  0.0344,  0.0313,
                       0.0209,  0.0312,  0.0275,  0.0334,  0.0340,  0.0237,  0.0161,
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                       0.0214,  0.0201,  0.0198,  0.0485,  0.0182,  0.0163,  0.0179,
                       0.0285,  0.0202,  0.0260,  0.0253,  0.0251,  0.0203,  0.0244,
                       0.0278,  0.0204,  0.0288,  0.0260,  0.0296,  0.0217,  0.0270,
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                       0.0216,  0.0265,  0.0192,  0.0270,  0.0228,  0.0215,  0.0201,
                       0.0290,  0.0211,  0.0199,  0.0288,  0.0232,  0.0280,  0.0216,
                       0.0193,  0.0217,  0.0177,  0.0211,  0.0440,  0.0199,  0.0259,
                       0.0392,  0.0232,  0.0210,  0.0233,  0.0231,  0.0222,  0.0305,
                       0.0171,  0.0164,  0.0244,  0.0189,  0.0237,  0.0241,  0.0326,
                       0.0236,  0.0187,  0.0171,  0.0252,  0.0203,  0.0162,  0.0237,
                       0.0249,  0.0185,  0.0223,  0.0197,  0.0219,  0.0231,  0.0218,
                       0.0260,  0.0280,  0.0356,  0.0185,  0.0173,  0.0152,  0.0242,
                       0.0225,  0.0258,  0.0377,  0.0205,  0.0227,  0.0168,  0.0169,
                       0.0282,  0.0174,  0.0163,  0.0199,  0.0275,  0.0154,  0.0225,
                       0.0324,  0.0261,  0.0295,  0.0168,  0.0258,  0.0208,  0.0225,
                       0.0345,  0.0199,  0.0173,  0.0218,  0.0220,  0.0144,  0.0200,
                       0.0207,  0.0255,  0.0164,  0.0301,  0.0328,  0.0208,  0.0339,
                       0.0288,  0.0368,  0.0217,  0.0218,  0.0202,  0.0364,  0.0279,
                       0.0281,  0.0265,  0.0169,  0.0191,  0.0346,  0.0256,  0.0462,
                       0.0185,  0.0216,  0.0234,  0.0271,  0.0330,  0.0167,  0.0175,
                       0.0254,  0.0202,  0.0226,  0.0235,  0.0179,  0.0246,  0.0182,
                       0.0314,  0.0258,  0.0252,  0.0424,  0.0317,  0.0201,  0.0204,
                       0.0252,  0.0200,  0.0161,  0.0222,  0.0247,  0.0289,  0.0284,
                       0.0232,  0.0242,  0.0230,  0.0157,  0.0240,  0.0156,  0.0228,
                       0.0181,  0.0231,  0.0246,  0.0236,  0.0202,  0.0172,  0.0323,
                       0.0165,  0.0147,  0.0398,  0.0178,  0.0254,  0.0206,  0.0157,
                       0.0351,  0.0445,  0.0250,  0.0210,  0.0216,  0.0315,  0.0409,
                       0.0267,  0.0172,  0.0223,  0.0205,  0.0168,  0.0253,  0.0199,
                       0.0180,  0.0190,  0.0192,  0.0161,  0.0280,  0.0283,  0.0206,
                       0.0241,  0.0204,  0.0534,  0.0199,  0.0219,  0.0314,  0.0183,
                       0.0196,  0.0200,  0.0280,  0.0187,  0.0263,  0.0202,  0.0226,
                       0.0203,  0.0216,  0.0279,  0.0163,  0.0209,  0.0250,  0.0251,
                       0.0229,  0.0204,  0.0293,  0.0229,  0.0252,  0.0141,  0.0194,
                       0.0280,  0.0190,  0.0256,  0.0242,  0.0265,  0.0186,  0.0156,
                       0.0202,  0.0259,  0.0222,  0.0184,  0.0330,  0.0264,  0.0232,
                       0.0234,  0.0291,  0.0176,  0.0238,  0.0261,  0.0214,  0.0302,
                       0.0296,  0.0249,  0.0161,  0.0216,  0.0172,  0.0229,  0.0324,
                       0.0299,  0.0194,  0.0260,  0.0164,  0.0317,  0.0191,  0.0225,
                       0.0210,  0.0295,  0.0248,  0.0190,  0.0150,  0.0365,  0.0234,
                       0.0206,  0.0464,  0.0318,  0.0161,  0.0211,  0.0249,  0.0264,
                       0.0194,  0.0204,  0.0118,  0.0169,  0.0167,  0.0337,  0.0251,
                       0.0224,  0.0239,  0.0211,  0.0204,  0.0172,  0.0245,  0.0293,
                       0.0242,  0.0164,  0.0241,  0.0261,  0.0207,  0.0188,  0.0169,
                       0.0187,  0.0387,  0.0180,  0.0229,  0.0280,  0.0321,  0.0317,
                       0.0210,  0.0193,  0.0220,  0.0199,  0.0165,  0.0279,  0.0320,
                       0.0526,  0.0348,  0.0259,  0.0211,  0.0199,  0.0271,  0.0392,
                       0.0472,  0.0244,  0.0295,  0.1376,  0.0211,  0.0188,  0.0158,
                       0.0179,  0.0262,  0.0233,  0.0211,  0.0334,  0.0174,  0.0166,
                       0.0274,  0.0190,  0.0284,  0.0187,  0.0129,  0.0176,  0.0142,
                       0.0178,  0.0373,  0.0160,  0.0312,  0.0552,  0.0255,  0.0193,
                       0.0202,  0.0343,  0.0284,  0.0223,  0.0354,  0.0149,  0.0194,
                       0.0259], device='cuda:0')),
             ('classifier.0.weight',
              tensor([[ 4.8425e-03,  2.0154e-03,  8.5743e-04,  ..., -1.4330e-03,
                       -1.1918e-02,  2.5657e-03],
                      [ 1.0697e-02, -1.1946e-02, -3.0395e-03,  ...,  8.6051e-04,
                        1.0442e-02, -8.7477e-03],
                      [ 1.9964e-03, -4.5422e-03, -1.2422e-02,  ..., -2.7622e-03,
                       -7.9099e-03, -4.3515e-03],
                      ...,
                      [-8.4592e-04, -2.1114e-03, -1.0877e-02,  ..., -4.5135e-03,
                       -1.3154e-02, -3.7412e-03],
                      [ 9.5506e-03, -6.7894e-03,  1.9150e-03,  ..., -5.6436e-03,
                        7.0148e-03, -2.4530e-03],
                      [ 1.3211e-02,  1.9533e-02,  3.8056e-02,  ..., -1.7007e-02,
                        4.5350e-03, -2.5297e-02]], device='cuda:0')),
             ('classifier.0.bias', tensor(1.00000e-02 *
                     [-0.6802, -0.9341, -0.3885,  ..., -1.3350, -0.8487, -0.0277], device='cuda:0')),
             ('classifier.3.weight',
              tensor([[ 6.1619e-03,  6.4476e-04,  8.3916e-04,  ...,  2.1259e-02,
                       -8.5731e-03, -1.6847e-02],
                      [ 4.0635e-03,  1.5604e-02,  3.0958e-03,  ...,  8.1880e-03,
                        1.0659e-02,  6.0409e-03],
                      [-1.5112e-03, -2.0020e-02, -4.1926e-05,  ..., -1.3278e-02,
                       -1.4948e-02, -4.2093e-02],
                      ...,
                      [ 1.1850e-02, -1.2043e-02, -1.3692e-02,  ...,  4.0007e-03,
                       -5.2164e-03, -5.9656e-03],
                      [-2.0445e-03, -2.1446e-02,  4.4284e-04,  ...,  5.7744e-03,
                       -9.2461e-03,  3.6632e-02],
                      [-5.8537e-03,  1.4070e-02,  1.7734e-02,  ...,  7.4095e-03,
                        4.2703e-03, -3.4650e-02]], device='cuda:0')),
             ('classifier.3.bias', tensor(1.00000e-02 *
                     [ 0.1486, -1.1374, -4.3219,  ..., -0.7325, -0.6860, -0.0474], device='cuda:0')),
             ('classifier.6.weight',
              tensor([[-3.4861e-02, -1.6712e-02, -3.0446e-02,  ..., -2.3195e-02,
                       -1.0470e-02, -5.6383e-02],
                      [-2.7809e-02,  3.0521e-03,  7.1013e-03,  ..., -8.6353e-03,
                        4.1804e-02,  3.8033e-02],
                      [ 6.0985e-03, -1.0467e-02,  3.6337e-02,  ..., -3.5249e-02,
                       -2.7527e-03, -7.6805e-02],
                      ...,
                      [-3.1551e-02,  2.1154e-02, -5.0123e-02,  ...,  2.8941e-02,
                       -1.2157e-02,  2.0762e-02],
                      [ 2.0667e-02, -1.3924e-02, -2.6958e-02,  ...,  2.1151e-03,
                       -3.8191e-02,  2.8399e-03],
                      [ 1.6890e-02,  1.5822e-03, -3.7808e-02,  ..., -2.3673e-02,
                       -3.0144e-03, -1.4710e-02]], device='cuda:0')),
             ('classifier.6.bias', tensor(1.00000e-02 *
                     [ 0.1135, -2.7566,  2.3163, -0.5751, -4.0288,  2.1770, -1.6820,
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                       2.9053,  0.8460,  0.4576,  0.5132, -3.4900, -1.2879,  0.2990,
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                       1.7732, -0.6145,  0.4239, -1.7349], device='cuda:0'))])
In [69]:
# save state dict only
# torch.save(model.state_dict(), 'checkpoint_image_classifier_state_dict.pth')
In [18]:
"""save checkpoint
I've commented it out not to accidentally overwrite my existing checkpoint of the model I trained
I've also backed up the checkpoint
"""
# model.to('cpu')
# torch.save({
#             'model_state_dict': model.state_dict(),         
#             'optimizer_state_dict': optimizer.state_dict(),
#             'classifier': model.classifier,        
#             'structure': 'vgg19_bn',
#             'epochs': epochs,
#             'class_to_idx': model.class_to_idx
#             }, 'checkpoint_image_classifier.pth')
Out[18]:
"save checkpoint\nI've commented it out not to accidentally overwrite my existing checkpoint\nI've also backed up the checkpoint\n"

Loading the checkpoint

At this point it's good to write a function that can load a checkpoint and rebuild the model. That way you can come back to this project and keep working on it without having to retrain the network.

In [ ]:
# load state dict only
# PATH_state_dict = 'checkpoint_image_classifier_state_dict.pth'
# model.load_state_dict(torch.load(PATH_state_dict))
# model.eval()
In [7]:
def load_Model():
    
    global model, device
    
    # Use GPU if it's available
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = models.vgg19_bn()

    criterion = nn.NLLLoss()

    PATH = 'checkpoint_image_classifier.pth'
    
    #https://discuss.pytorch.org/t/problem-loading-model-trained-on-gpu/17745
    try:
        checkpoint = torch.load(PATH)
    except: 
        checkpoint = torch.load(PATH, map_location=lambda storage, loc: storage)

    optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)

    model.optimizer_state_dict = checkpoint['optimizer_state_dict']
    model.classifier = checkpoint['classifier'] 
    model.load_state_dict(checkpoint['model_state_dict'])
    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
    model.class_to_idx = checkpoint['class_to_idx']

    model.to(device);

    model.eval()
    
load_Model()    
In [17]:
# did I load the model?
model
Out[17]:
VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace)
    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU(inplace)
    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (9): ReLU(inplace)
    (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (12): ReLU(inplace)
    (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (16): ReLU(inplace)
    (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (19): ReLU(inplace)
    (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (22): ReLU(inplace)
    (23): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (24): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (25): ReLU(inplace)
    (26): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (27): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (29): ReLU(inplace)
    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (32): ReLU(inplace)
    (33): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (34): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (35): ReLU(inplace)
    (36): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (37): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (38): ReLU(inplace)
    (39): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (42): ReLU(inplace)
    (43): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (44): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (45): ReLU(inplace)
    (46): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (47): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (48): ReLU(inplace)
    (49): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (50): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (51): ReLU(inplace)
    (52): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (classifier): Sequential(
    (0): Linear(in_features=25088, out_features=4096, bias=True)
    (1): ReLU()
    (2): Dropout(p=0.2)
    (3): Linear(in_features=4096, out_features=1024, bias=True)
    (4): ReLU()
    (5): Dropout(p=0.2)
    (6): Linear(in_features=1024, out_features=102, bias=True)
    (7): LogSoftmax()
  )
)
In [18]:
model.state_dict ()
Out[18]:
OrderedDict([('features.0.weight',
              tensor([[[[-3.2130e-08, -3.1717e-08, -6.7120e-08],
                        [-2.9321e-08, -2.1596e-08, -5.3207e-08],
                        [-5.3304e-08, -4.0832e-08, -6.6434e-08]],
              
                       [[ 1.3710e-07,  1.2848e-07,  7.1270e-08],
                        [ 1.5487e-07,  1.5186e-07,  8.3582e-08],
                        [ 9.3392e-08,  9.9451e-08,  4.1284e-08]],
              
                       [[ 1.7435e-07,  1.7840e-07,  1.0561e-07],
                        [ 1.9644e-07,  1.9769e-07,  1.2560e-07],
                        [ 1.1935e-07,  1.3447e-07,  5.9638e-08]]],
              
              
                      [[[-6.3467e-08, -5.2714e-08, -3.8511e-08],
                        [-1.4349e-08, -9.0426e-09, -1.7164e-08],
                        [ 2.2282e-08,  1.9084e-08,  1.8150e-09]],
              
                       [[-1.2191e-08, -6.1933e-09,  8.5022e-09],
                        [ 3.8618e-08,  5.8869e-08,  2.2082e-08],
                        [ 6.2501e-08,  7.0528e-08,  2.7185e-08]],
              
                       [[ 3.0045e-08,  3.8003e-08,  3.5389e-08],
                        [ 8.6743e-08,  1.1626e-07,  8.0502e-08],
                        [ 7.9653e-08,  1.0057e-07,  6.7894e-08]]],
              
              
                      [[[-1.3565e-01, -2.9960e-01, -1.5151e-01],
                        [-2.6804e-01, -3.0505e-01, -9.4600e-02],
                        [-1.4124e-01, -8.1448e-02,  1.7112e-01]],
              
                       [[ 2.0333e-01,  4.0123e-01,  1.3747e-01],
                        [ 4.2157e-01,  6.7595e-01,  3.2725e-01],
                        [ 1.3103e-01,  3.2654e-01,  6.4284e-02]],
              
                       [[-4.1092e-02,  4.6723e-02, -9.7236e-03],
                        [ 2.9558e-02,  3.5226e-02, -1.2338e-01],
                        [-9.1504e-02, -1.7836e-01, -3.1635e-01]]],
              
              
                      ...,
              
              
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             ('classifier.6.bias', tensor(1.00000e-02 *
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                      -1.2655,  3.5463,  1.1575, -0.3702, -0.5052,  1.4868, -2.1086,
                      -2.7435, -0.6051, -2.1503, -0.0862,  1.7366, -0.0730, -0.0773,
                      -2.9996,  2.0516, -0.6719,  3.9243,  3.4918, -3.8858, -2.5822,
                       2.9053,  0.8460,  0.4576,  0.5132, -3.4900, -1.2879,  0.2990,
                       2.2238,  2.0982,  0.3468, -4.0205,  0.5962, -3.0963, -2.8138,
                       1.6033, -1.2701, -1.6969,  2.3687, -0.3530,  1.0361,  2.0927,
                      -1.1049,  0.2565, -1.4446, -0.4767,  2.8264,  0.6477, -0.8600,
                      -5.2576, -2.4751, -0.3216, -2.6369,  0.4377, -1.5744, -0.7534,
                       1.2630, -2.1028,  3.4669,  1.8193,  0.0111,  2.1553, -2.0185,
                       1.3188, -2.1147,  0.7303,  2.5368,  1.3857,  2.9217, -0.0603,
                      -2.3172,  1.5295,  0.7743, -0.8061, -1.6067, -3.1090,  3.4488,
                      -3.5163,  0.3065,  0.5305,  2.0586, -1.8557,  0.0267, -0.0209,
                      -4.8240, -0.4410, -0.4267, -0.2445,  2.5738, -4.4996, -1.1610,
                       1.7732, -0.6145,  0.4239, -1.7349]))])
In [27]:
# testing the model accuracy again to make sure it loaded fine
# this only appears to work when I'm in GPU mode. On CPU, it takes forever to respond, I had to interrupt the kernet. 
# So only test it in GPU
# if you wanna test it on CPU, run individual files thru the model, it works fine
model.to(device)
test_Model()
torch.Size([64, 102])
tensor([[ 74],
        [ 32],
        [ 70],
        [ 55],
        [  3],
        [ 76],
        [ 96],
        [ 52],
        [ 54],
        [ 78]], device='cuda:0')
Accuracy: 85.9375%
In [19]:
torch.cuda.is_available()
Out[19]:
False

Inference for classification

Now you'll write a function to use a trained network for inference. That is, you'll pass an image into the network and predict the class of the flower in the image. Write a function called predict that takes an image and a model, then returns the top $K$ most likely classes along with the probabilities. It should look like

probs, classes = predict(image_path, model)
print(probs)
print(classes)
> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]
> ['70', '3', '45', '62', '55']

First you'll need to handle processing the input image such that it can be used in your network.

Image Preprocessing

You'll want to use PIL to load the image (documentation). It's best to write a function that preprocesses the image so it can be used as input for the model. This function should process the images in the same manner used for training.

First, resize the images where the shortest side is 256 pixels, keeping the aspect ratio. This can be done with the thumbnail or resize methods. Then you'll need to crop out the center 224x224 portion of the image.

Color channels of images are typically encoded as integers 0-255, but the model expected floats 0-1. You'll need to convert the values. It's easiest with a Numpy array, which you can get from a PIL image like so np_image = np.array(pil_image).

As before, the network expects the images to be normalized in a specific way. For the means, it's [0.485, 0.456, 0.406] and for the standard deviations [0.229, 0.224, 0.225]. You'll want to subtract the means from each color channel, then divide by the standard deviation.

And finally, PyTorch expects the color channel to be the first dimension but it's the third dimension in the PIL image and Numpy array. You can reorder dimensions using ndarray.transpose. The color channel needs to be first and retain the order of the other two dimensions.

In [8]:
def process_image(image):
    ''' Open, transform (resize, centre crop, normalize (between zero and 1 ), convert to a Numpy array)
    '''
    # Converting image to PIL image using image file path
    PIL_Image = Image.open(f'{image}')

    # Building image transform
    """this transform is taken from Udacit intro to Pytorch lesson"""
    transform = transforms.Compose([transforms.Resize(256),
                                    transforms.CenterCrop(224),
                                    transforms.ToTensor(),
                                    transforms.Normalize([0.485, 0.456, 0.406], 
                                                         [0.229, 0.224, 0.225])]) 
    
    ## Transforming image for use with network
    PIL_Transformed = transform(PIL_Image)
    
    # Converting to Numpy array 
    """https://kite.com/python/examples/4887/pil-convert-between-a-pil-%60image%60-and-a-numpy-%60array%60"""
    numpy_array = np.array(PIL_Transformed)
    
    return numpy_array
In [9]:
def imshow(image, ax=None, title=None):
    if ax is None:
        fig, ax = plt.subplots()
    
    # PyTorch tensors assume the color channel is the first dimension
    # but matplotlib assumes is the third dimension
    #print('image shape before transpost', image.shape)
    image = image.transpose((1, 2, 0))
    #print('image shape after transpose', image.shape)
    
    # Undo preprocessing
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    image = std * image + mean
    
    # Image needs to be clipped between 0 and 1 or it looks like noise when displayed
    image = np.clip(image, 0, 1)
    
    ax.imshow(image)
    
    return ax
In [10]:
# Test image
image_name = data_dir + '/test' + '/1/' + 'image_06743.jpg'
img_test =  process_image(image_name)

# Check to see if image pre-processing was successful
imshow(img_test, ax=None, title=None)
Out[10]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f620631e4e0>
In [11]:
# Test a bunch of images
for image in glob.glob((data_dir + '/test/1/*')):
    print (image)
    my_processed_image =  process_image(image)
    imshow(my_processed_image)
flowers/test/1/image_06760.jpg
flowers/test/1/image_06754.jpg
flowers/test/1/image_06743.jpg
flowers/test/1/image_06764.jpg
flowers/test/1/image_06752.jpg
In [17]:
imshow(process_image('flowers/test/1/image_06764.jpg'))
Out[17]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fa79816af28>

To check your work, the function below converts a PyTorch tensor and displays it in the notebook. If your process_image function works, running the output through this function should return the original image (except for the cropped out portions).

Class Prediction

Once you can get images in the correct format, it's time to write a function for making predictions with your model. A common practice is to predict the top 5 or so (usually called top-$K$) most probable classes. You'll want to calculate the class probabilities then find the $K$ largest values.

To get the top $K$ largest values in a tensor use x.topk(k). This method returns both the highest k probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using class_to_idx which hopefully you added to the model or from an ImageFolder you used to load the data (see here). Make sure to invert the dictionary so you get a mapping from index to class as well.

Again, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.

probs, classes = predict(image_path, model)
print(probs)
print(classes)
> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]
> ['70', '3', '45', '62', '55']
In [12]:
def predict(image_path, model=model, topk=5):
    ''' Predict flower species.
        To be honest, I struggled with this part and got stuck so badly, that I had to take a peek at waht other students had submitted
        The code inn this cell  is adapted from:
        ttps://github.com/S-Tabor/udacity-image-classifier-project/blob/master/Image%20Classifier%20Project.ipynb
        by S-Tabor
        I didn't copy paste the code, but I got the general idea for implementation.
        
        I made sure to avoid plagiarism
        
        https://udacity.zendesk.com/hc/en-us/articles/360001451091-What-is-plagiarism-
        Not Plagiarism:
        Looking at someone else’s code to get a general idea of implementation, then putting it away and starting to write your own code from scratch.
    '''
    #process the image
    img = process_image(image_path)
    
   # Converting to torch tensor from Numpy array
    #https://discuss.pytorch.org/t/how-to-convert-array-to-tensor/28809
    #https://discuss.pytorch.org/t/difference-between-tensor-and-torch-floattensor/24448/2

    img_tensor = torch.from_numpy(img).type(torch.FloatTensor)
    
    # Adding a dimension to image
    #https://discuss.pytorch.org/t/what-is-the-difference-between-view-and-unsqueeze/1155

    img_add_dim = img_tensor.unsqueeze_(0)

    # Setting model to evaluation mode and turning off gradients to speed up the following step
    model.eval()
    
    with torch.no_grad():
        # Running image through network
        try:
            output = model(img_add_dim)
        except:
            output = model(img_add_dim.cuda()) 

    # probabilities
    probabilities = torch.exp(output)
    # print(probabilities)
    top_probabilities = probabilities.topk(topk)[0]
    top_indexes = probabilities.topk(topk)[1]
    
    # Converting probabilities and outputs to numpy arrays
    probs_top_list = np.array(top_probabilities)[0]
    # print(type(probs_top_list))
    index_top_list = np.array(top_indexes[0])
    
    # Loading index and class mapping
    class_to_idx = model.class_to_idx
    # Inverting index-class dictionary
    #https://stackoverflow.com/questions/483666/reverse-invert-a-dictionary-mapping
    indx_to_class = {A: B for B, A in class_to_idx.items()}

    # Converting index list to class list
    classes_top_list = []
    for index in index_top_list:
        classes_top_list += [indx_to_class[index]]

    print(image_path)
    print('top probs', probs_top_list)
    print('top_classes',classes_top_list)

    return probs_top_list, classes_top_list

# testing if the actual label which I entered matches the predicted one
# import glob
# for filepath in glob.glob((data_dir + '/test/96/*')):
#     predict(filepath, model=model, topk=5)
In [13]:
predict('flowers/test/30/image_03481.jpg')
flowers/test/30/image_03481.jpg
top probs [  9.86215413e-01   1.26778614e-02   6.38392114e-04   1.63586883e-04
   1.45571292e-04]
top_classes ['30', '27', '72', '92', '82']
Out[13]:
(array([  9.86215413e-01,   1.26778614e-02,   6.38392114e-04,
          1.63586883e-04,   1.45571292e-04], dtype=float32),
 ['30', '27', '72', '92', '82'])
In [14]:
for name in glob.glob((data_dir + '/test/66/*')):
    predict(name, topk=1)
flowers/test/66/image_05537.jpg
top probs [ 0.99731314]
top_classes ['66']
flowers/test/66/image_05582.jpg
top probs [ 0.99545413]
top_classes ['66']
flowers/test/66/image_05562.jpg
top probs [ 0.99719405]
top_classes ['66']
flowers/test/66/image_05549.jpg
top probs [ 0.99983472]
top_classes ['66']
In [31]:
Myfilepath='flowers/test/30/image_03538.jpg'
# print(Myfilepath)
predict(image_path=Myfilepath, model=model, topk=1)
flowers/test/30/image_03538.jpg
top probs [ 0.99615127]
top_classes ['30']
Out[31]:
(array([ 0.99615127], dtype=float32), ['30'])
In [32]:
cat_to_name['1']
Out[32]:
'pink primrose'
In [33]:
for image in glob.glob((data_dir + '/test/64/*')):
    my_processed_image =  process_image(image)
    imshow(my_processed_image)
    predict(image)
flowers/test/64/image_06099.jpg
top probs [  9.94310141e-01   1.56016741e-03   1.09841069e-03   7.83293741e-04
   6.93015987e-04]
top_classes ['64', '32', '53', '51', '76']
flowers/test/64/image_06130.jpg
top probs [  9.84275758e-01   1.23303989e-02   2.64858012e-03   3.03940789e-04
   1.85715384e-04]
top_classes ['64', '51', '76', '32', '65']
flowers/test/64/image_06138.jpg
top probs [ 0.97947115  0.00956609  0.00597732  0.0016611   0.00158365]
top_classes ['64', '65', '67', '42', '34']
flowers/test/64/image_06104.jpg
top probs [ 0.73628443  0.20635158  0.03331089  0.01019444  0.00437504]
top_classes ['64', '76', '55', '19', '65']
flowers/test/64/image_06134.jpg
top probs [  9.45744574e-01   4.98634055e-02   2.02328758e-03   1.39389327e-03
   4.48154111e-04]
top_classes ['64', '76', '34', '65', '42']
In [26]:
predict('flowers/test/1/image_06760.jpg')
flowers/test/1/image_06760.jpg
top probs [ 0.4002547   0.34357026  0.09689626  0.08575003  0.0340166 ]
top_classes ['72', '95', '40', '96', '97']
Out[26]:
(array([ 0.4002547 ,  0.34357026,  0.09689626,  0.08575003,  0.0340166 ], dtype=float32),
 ['72', '95', '40', '96', '97'])

Sanity Checking

Now that you can use a trained model for predictions, check to make sure it makes sense. Even if the testing accuracy is high, it's always good to check that there aren't obvious bugs. Use matplotlib to plot the probabilities for the top 5 classes as a bar graph, along with the input image. It should look like this:

You can convert from the class integer encoding to actual flower names with the cat_to_name.json file (should have been loaded earlier in the notebook). To show a PyTorch tensor as an image, use the imshow function defined above.

In [16]:
# cat_to_name
In [15]:
def check_sanity(folder = 30, image_number = '03466'):
#https://matplotlib.org/3.1.0/gallery/pyplots/pyplot_two_subplots.html
#https://stackoverflow.com/questions/24644656/how-to-print-pandas-dataframe-without-index
#https://stackoverflow.com/questions/30522724/take-multiple-lists-into-dataframe

    path = test_dir + '/' +str(folder)+ '/image_' + image_number +'.jpg'

    image = process_image(path)
    
    probabilities, classes = predict(path, model)
    
  
    class_names = []
    for i in classes:
        class_names += [cat_to_name[i]]
        

    df = pd.DataFrame(
    {'classes': classes,
     'class_names': class_names,
     'probabilities': probabilities 
    })
    
    plt.rcParams["figure.figsize"] = (10,5)
    plt.figure()

    title = cat_to_name[str(folder)]

    ax0 =imshow(image)
    ax0.set_title(title)
    
    plt.figure(figsize = (10,5))
    ax1 = sns.barplot(y="class_names", x="probabilities", data=df)
    
    plt.show()
    
    print (df.to_string(index=False))
    
check_sanity()    
flowers/test/30/image_03466.jpg
top probs [  9.96831357e-01   2.07501207e-03   8.09328572e-04   1.29197128e-04
   5.72931094e-05]
top_classes ['30', '82', '32', '38', '1']
<matplotlib.figure.Figure at 0x7f6202899668>
classes       class_names  probabilities
    30     sweet william       0.996831
    82          clematis       0.002075
    32      garden phlox       0.000809
    38  great masterwort       0.000129
     1     pink primrose       0.000057
In [18]:
check_sanity(folder = 30, image_number = '03528')
flowers/test/30/image_03528.jpg
top probs [  9.99993920e-01   4.25792041e-06   1.29685350e-06   2.85470236e-07
   1.49205505e-07]
top_classes ['30', '51', '94', '32', '82']
<matplotlib.figure.Figure at 0x7f6202974710>
classes    class_names  probabilities
    30  sweet william   9.999939e-01
    51        petunia   4.257920e-06
    94       foxglove   1.296853e-06
    32   garden phlox   2.854702e-07
    82       clematis   1.492055e-07
In [117]:
for name in glob.glob((data_dir + '/test/66/*')):
    print(name)
flowers/test/66/image_05537.jpg
flowers/test/66/image_05582.jpg
flowers/test/66/image_05562.jpg
flowers/test/66/image_05549.jpg
In [17]:
def check_sanity_whole_folder(folder = 1):
#https://matplotlib.org/3.1.0/gallery/pyplots/pyplot_two_subplots.html
#https://stackoverflow.com/questions/24644656/how-to-print-pandas-dataframe-without-index
#https://stackoverflow.com/questions/30522724/take-multiple-lists-into-dataframe

    for name in glob.glob((data_dir + '/test/'+  str(folder) + '/*')):
    
        path = name

        image = process_image(path)

        probabilities, classes = predict(path, model)

        class_names = []
        for i in classes:
            class_names += [cat_to_name[i]]

        df = pd.DataFrame(
        {'classes': classes,
         'class_names': class_names,
         'probabilities': probabilities 
        })

        plt.rcParams["figure.figsize"] = (10,5)
        plt.figure()

        title = cat_to_name[str(folder)]

        ax0 =imshow(image)
        ax0.set_title(title)

        plt.figure(figsize = (10,5))

        ax1 = sns.barplot(y="class_names", x="probabilities", data=df)

        plt.show()
In [31]:
# check_sanity_whole_folder()

hard ones:

'4': 'sweet pea' - got most of them wrong

'53': 'primula' is a hard one to recognize, there were quite a few error

'83': 'hibiscus' is a relative hard one too, they look different, different colours

easy ones:

'71': 'gazania' - confidently gets all of them right

'25': 'grape hyacinth',

'66': 'osteospermum',

'13': 'king protea',

In [19]:
check_sanity_whole_folder(13)   
flowers/test/13/image_05767.jpg
top probs [  9.91392374e-01   3.21134622e-03   2.31901277e-03   1.11253397e-03
   5.08167141e-04]
top_classes ['13', '18', '29', '73', '39']
<matplotlib.figure.Figure at 0x7f62029d26d8>
flowers/test/13/image_05775.jpg
top probs [  9.99929130e-01   4.18586933e-05   1.00311699e-05   6.85777741e-06
   4.88819569e-06]
top_classes ['13', '29', '21', '77', '18']
<matplotlib.figure.Figure at 0x7f620634e908>
flowers/test/13/image_05761.jpg
top probs [  5.42159617e-01   4.56748009e-01   9.99706215e-04   8.57696068e-05
   3.20615459e-06]
top_classes ['13', '29', '73', '18', '31']
<matplotlib.figure.Figure at 0x7f62029dedd8>
flowers/test/13/image_05745.jpg
top probs [  9.99884248e-01   3.12176489e-05   2.58994496e-05   2.29517900e-05
   1.19559218e-05]
top_classes ['13', '92', '100', '41', '29']
<matplotlib.figure.Figure at 0x7f62042f42e8>
flowers/test/13/image_05769.jpg
top probs [  9.99045432e-01   7.12506997e-04   2.08429396e-04   1.57285976e-05
   4.92571235e-06]
top_classes ['13', '18', '91', '77', '73']
<matplotlib.figure.Figure at 0x7f62028e2550>
flowers/test/13/image_05787.jpg
top probs [ 0.86515272  0.11042858  0.01281158  0.00439596  0.00217577]
top_classes ['13', '29', '91', '39', '18']
<matplotlib.figure.Figure at 0x7f620261ef60>